Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-23T10:28:28.142Z Has data issue: false hasContentIssue false
  • English
  • Français

Temporal dynamics of 2D and 3D shape representation in macaque visual area V4

Published online by Cambridge University Press:  04 October 2006

JAY HEGDÉ  and
DAVID C. VAN ESSEN
JAY HEGDÉ
Affiliation:
Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri Current address of Jay Hegdé: Department of Psychology, University of Minnesota, Minneapolis, MN 55455
DAVID C. VAN ESSEN
Affiliation:
Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri
Get access Rights & Permissions [Opens in a new window]

Abstract

We studied the temporal dynamics of shape representation in area V4 of the alert macaque monkey. Analyses were based on two large stimulus sets, one equivalent to the 2D shape stimuli used in a previous study of V2, and the other a set of stereoscopic 3D shape stimuli. As in V2, we found that information conveyed by individual V4 neurons about the stimuli tended to be maximal during the initial transient response and generally lower, albeit statistically significant, afterwards. The population response was substantially correlated from one stimulus to the next during the transients, and decorrelated as responses decayed. V4 responses showed significantly longer latencies than in V2, especially for the 3D stimulus set. Recordings from area V1 in a single animal revealed temporal dynamic patterns in response to the 2D shape stimuli that were largely similar to those in V2 and V4. Together with earlier results, these findings provide evidence for a distributed process of coarse-to-fine representation of shape stimuli in the visual cortex.

Keywords

DecorrelationDimensionality reductionMetric multidimensional scalingOnset transientsRate codingSignal-to-noise ratio.
Type
Research Article
Information
Visual Neuroscience , Volume 23 , Issue 5 , September 2006 , pp. 749 - 763
Copyright
2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Brockwell, P.J. & Davis, R.A. (2002). Introduction to Time Series and Forecasting, 2nd ed. New York: Springer.CrossRef
Carpenter, G.A. & Grossberg, S. (1987). A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision Graph Image Process 37, 54–115.CrossRefGoogle Scholar
Chatfield, S. (2003). The Analysis of Time Series: An Introduction. 6th ed. Boca Raton, FL: Chapman and Hall/CRC Press.
Dan, Y., Atick, J.J., & Reid, R.C. (1996). Efficient coding of natural scenes in the lateral geniculate nucleus: Experimental test of a computational theory. Journal of Neuroscience 16, 3351–3362.Google Scholar
DeAngelis, G.C. & Uka, T. (2003). Coding of horizontal disparity and velocity by MT neurons in the alert macaque. Journal of Neurophysiology 89, 1094–1111.Google Scholar
Donders, F.C. (1969). On the speed of mental processes. Acta Psychologica 30, 412–431.CrossRefGoogle Scholar
Edwards, R., Xiao, D., Keysers, C., F öldiák, P., & Perrett, D. (2003). Color sensitivity of cells responsive to complex stimuli in the temporal cortex. Journal of Neurophysiology 90, 1245–1256.CrossRefGoogle Scholar
Ferrera, V.P., Nealey, T.A., & Maunsell, J.H. (1992). Mixed parvocellular and magnocellular geniculate signals in visual area V4. Nature 358, 756–761.CrossRefGoogle Scholar
Friedrich, R.W. & Laurent, G. (2001). Dynamic optimization of odor representations by slow temporal patterning of mitral cell activity. Science 291, 889–894.CrossRefGoogle Scholar
Friedrich, R.W. & Laurent, G. (2004). Dynamics of olfactory bulb input and output activity during odor stimulation in zebrafish. Journal of Neurophysiology 91, 2658–2669.CrossRefGoogle Scholar
Gattass, R., Sousa, A.P.B., & Gross, C.G. (1988). Visuotopic organization and extent of V3 and V4 of the macaque. Journal of Neuroscience 8, 1831–1845.Google Scholar
Hegdé, J. & Van Essen, D.C. (2003). Strategies of shape representation in macaque visual area V2. Selectivity for complex shapes in primate visual area V2. Visual Neuroscience 20, 313–328.CrossRefGoogle Scholar
Hegdé, J. & Van Essen, D.C. (2004). Temporal dynamics of shape analysis in macaque visual area V2. Journal of Neurophysiology 92, 303–3042.Google Scholar
Hegdé, J. & Van Essen, D.C. (2005). Role of primate visual area V4 in the processing of 3D shape characteristics defined by disparity. Journal of Neurophysiology 94, 2856–2866.CrossRefGoogle Scholar
Hegdé, J. & Van Essen, D.C. (2006). A comparative study of shape representation in macaque visual areas V2 and V4. http://cercor.oxfordjournals.org/cgi/reprint/bh102v1.
Luce, R.D. (1986). Response Times: Their Role in Inferring Elementary Operations. Oxford: Oxford University Press.
McAdams, C.J. & Maunsell, J.H.R. (1999). Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. Journal of Neuroscience 19, 431–441.Google Scholar
Maunsell, J.H. & Gibson, J.R. (1992). Visual response latencies in striate cortex of the macaque monkey. Journal of Neurophysiology 68, 1332–1344.Google Scholar
Menz, M.D. & Freeman, R.D. (2003). Stereoscopic depth processing in the visual cortex: A coarse-to-fine mechanism. Nature Neuroscience 6, 59–65.Google Scholar
Menz, M.D. & Freeman, R.D. (2004). Temporal dynamics of binocular disparity processing in the central visual pathway. Journal of Neurophysiology 91, 1782–1793.CrossRefGoogle Scholar
Müller, J.R., Metha, A.B., Krauskopf, J., & Lennie, P. (2001). Information conveyed by onset transients in responses of striate cortical neurons. Journal of Neuroscience 21, 6978–6990.Google Scholar
Nowak, L.G. & Bullier, J. (1997). The timing of information transfer in the visual system. In Extrastriate Cortex in Primates, ed. Kaas, J., Rockland, K. & Peters, A. pp. 205–241. New York: Plenum.
Nowak, L.G., Munk, M.H., Girard, P., & Bullier, J. (1995). Visual latencies in areas V1 and V2 of the macaque monkey. Visual Neuroscience 12, 371–384.CrossRefGoogle Scholar
Palanca, B.J. & DeAngelis, G.C. (2003). Macaque middle temporal neurons signal depth in the absence of motion. Journal of Neuroscience 23, 7647–7658.Google Scholar
Panzeri, S. (1996). Quantitative methods for analyzing information processing in the mammalian cortex. Trieste, Italy: International School for Advanced Studies, Ph.D. Thesis.
Panzeri, S. & Treves, A. (1996). Analytical estimates of limited sampling biases in different information measures. Network: Computation in Neural Systems 7, 87–107.CrossRefGoogle Scholar
Perge, J.A., Borghuis, B.G., Bours, R.J., Lankheet, M.J., & van Wezel, R.J. (2005). Dynamics of directional selectivity in MT receptive field centre and surround. European Journal of Neuroscience 22, 2049–2058.CrossRefGoogle Scholar
Posner, M.I. (1978). Chronometric Explorations of Mind. Hillsdale, NJ: Erlbaum.
Prince, S.J., Pointon, A.D., Cumming, B.G., & Parker, A.J. (2002). Quantitative analysis of the responses of V1 neurons to horizontal disparity in dynamic random-dot stereograms. Journal of Neurophysiology 87, 191–208.CrossRefGoogle Scholar
Rieke, F., Warland, D., de Ruyter van Steveninck, R., & Bialek, W. (1997). Spikes. Cambridge, MA: MIT Press.
Ringach, D.L. (2003). Look at the big picture (details will follow). Nature Neuroscience 6, 7–8.CrossRefGoogle Scholar
Ringach, D.L., Hawken, M.J., & Shapley, R. (1997). Dynamics of orientation tuning in macaque primary visual cortex. Nature 387, 281–284.CrossRefGoogle Scholar
Roweis, S.T. & Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326.CrossRefGoogle Scholar
Schmolesky, M.T., Wang, Y., Hanes, D.P., Thompson, K.G., Leutgeb, S., Schall, J.D., & Leventhal, A.G. (1998). Signal timing across the macaque visual system. Journal of Neurophysiology 79, 3272–3278.Google Scholar
Schroeder, C.E., Mehta, A.D., & Givre, S.J. (1998). A spatiotemporal profile of visual system activation revealed by current source density analysis in the awake macaque. Cerebral Cortex 8, 575–592.CrossRefGoogle Scholar
Schyns, P.G. & Oliva, A. (1994). From blobs to boundary edges: evidence for time and scale dependent scene recognition. Psychological Science 5, 195–200.CrossRefGoogle Scholar
Seung, H.S. & Lee, D.D. (2000). The manifold ways of perception. Science 290, 2268–2269.CrossRefGoogle Scholar
Sugase, Y., Yamane, S., Ueno, S., & Kawano, K. (1999). Global and fine information coded by single neurons in the temporal visual cortex. Nature 400, 869–873.CrossRefGoogle Scholar
Tenenbaum, J.B., de Silva, V., & Langford, J.C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323.CrossRefGoogle Scholar
Ullman, S. (1995). Sequence seeking and counter streams—a computational model for bi-directional information flow in the visual-cortex. Cerebral Cortex 5, 1–11.Google Scholar
Van Essen, D.C. & Zeki, S. (1978). The topographic organization of rhesus monkey prestriate cortex. Journal of Physiology 277, 193–226.CrossRefGoogle Scholar
Wang, Y., Zhou, Y., Ma, Y., & Leventhal, A.G. (2005). Degradation of signal timing in cortical areas V1 and V2 of senescent monkeys. Cerebral Cortex 15, 403–408.CrossRefGoogle Scholar
Zohary, E., Hillman, P., & Hochstein, S. (1990). Time course of perceptual discrimination and single neuron reliability. Biological Cybernetics 2, 475–486.CrossRefGoogle Scholar